Systems Engineering

Understand DNS through clear visual explanations of name resolution, zones, records, caching, security, and practical operations.

Build your Linux foundation through visual explanations of processes, files, permissions, networking, storage, and system operations.

Understand IT service management through visual explanations of value, practices, workflows, and everyday engineering decisions.

Learn Microsoft Intune through visual explanations of enrollment, device configuration, applications, compliance, and endpoint operations.

Build a practical foundation in identities, authentication, authorization, credentials, and access management with clear visual explanations.

Understand Google Cloud through clear diagrams of identity, networks, compute, data, security, and everyday operations.

Learn the C++ fundamentals that connect source code, memory, ownership, build tools, and operating-system boundaries. Clear diagrams and small examples guide you toward reading, testing, and diagnosing systems-oriented programs.

Understand how Azure services fit together, then design, implement, and troubleshoot practical cloud systems. Visual explanations connect identity, governance, networking, applications, automation, observability, resilience, and day-2 operations.

We will define the developer contract first, and we will then build the platform underneath it together, step by step, using decisions that hold up in regulated environments and in small startups alike. Along the way, we will provision clusters, wire delivery, publish a developer API, enforce policy and ship observability, and we will do all of it where you can see every command, every manifest and every failure. Nothing here stays theoretical, because platform problems only show their true shape where an abstraction meets a real cluster.

Building reliable distributed systems is harder than it looks. This book takes you from the fundamentals of message queues to the realities of distributed transactions, with working code, practical patterns and production lessons to guide you. Learn how to handle failures, maintain consistency and build systems that keep working when things go wrong.

Build AI inference systems that run fast, use less memory and keep your data where it belongs. This hands-on guide walks you through model deployment, performance tuning, security and production engineering with practical code and real benchmarks. No cloud dependency, no surprise bills, just efficient AI systems you control.

What really makes software fast? This book takes you inside the modern CPU to show what happens to your code at the hardware level. Learn how to measure performance, find bottlenecks and turn slow code into faster code using practical techniques, real tools and real hardware.

Learn Go from language foundations to dependable command-line tools and production services. Visual explanations connect concurrency, networking, testing, security, diagnostics, and operations with practical implementation and troubleshooting.

HAProxy Essentials is a practical guide to building, running and troubleshooting HAProxy in production. From core concepts to advanced architecture, it explains how the pieces fit together and what can go wrong. Based on HAProxy 3.4 LTS, with clear notes on 2.8 and Enterprise-specific features.

What does it take to run an LLM on hardware that barely has enough memory to breathe? This practical guide takes you from transformer math and model compression to inference runtimes, benchmarking and real-world deployment, with hands-on case studies for building fully offline LLM systems on tiny devices.